ScholarWorks@중앙대학교

초록

Detecting Insider threats poses a major challenge due to the limitations of traditional security systems in identifying behavioral anomalies. This study investigated how stress-inducing factors—such as time pressure, workload, and task complexity—affected insider behavior and evaluated the effectiveness of non-invasive detection techniques. A meta-analysis of 30 empirical studies focused on behavioral changes under stress and on the performance of various detection methods including keystroke dynamics, heart rate variability, eye tracking, and electroencephalogram. The findings indicate that keystroke dynamics and heart rate variability are among the most accurate techniques, with highly stressed individuals being up to three times more likely to commit security violations. These results support the integration of stress-aware, non-invasive monitoring into organizational security systems. The study enhances human-centered cybersecurity by validating behavioral monitoring under stress and advocating multimodal approaches to real-time insider threat detection.

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